In recent years geology has become increasingly quantitative. Mathematical methods and software for structural reconstruction, sediment compaction, organic matter maturation, and hydrocarbon migration are now commonplace in hydrocarbon exploration, while geostatistical methods of reservoir characterization, reservoir flow modeling, and history matching have become essential tools in workflows to maximize returns in hydrocarbon production. This tendency toward quantification has been brought about by the availability of high-quality data such as 3D and 4D seismic, increased computer power, and an increasingly competitive environment in which smaller margins matter. Although data can still be uncertain (such as lithologies and petrophysical properties from seismic information), the uncertainty can be quantified. A relatively recent and still underused addition to the geologist’s set of quantitative tools has been geologic process modeling (or GPM, also called stratigraphic forward modeling). This technique aims to model the processes of erosion, transport and deposition of clastic sediments, as well as carbonate growth and redistribution on the basis of quantitative deterministic physical principles (Cross 1990; Tetzlaff & Priddy 2001; Merriam & Davis 2001). The results show the geometry and composition of the stratigraphic sequence as a consequence of sea-level change, paleogeography, paleoclimate, tectonics and variation in sediment input. In its scope, GPM is similar to detailed sequence stratigraphy. However, the latter has been developed on the basis of observations and inferences, mostly from seismic data, and conceptual models that specify what stratigraphic relationships should be expected under certain conditions (such as sea-level rise and fall, or variations in sediment input). GPM on the other hand, is based solely on numeric modeling of open-channel flow, currents, waves, and the movement of sediment. The observed stratigraphy is the result of modeling a physical system. During numeric modeling of sedimentation, the modeler is faced with assigning values to several parameters that are difficult to estimate (sediment input, sediment diffusion and transport coefficients, for various environments and sediment types). Even after a result has been achieved that approximately matches observations, the model is usually run several times varying the unknown parameters within ranges of uncertainty. The ranges of uncertainty are selected so that the differences between results and data do not exceed a predetermined value. The set of results is used for statistical inference of model results, providing information such as uncertainty in reservoir geometry and petrophysical parameters, as well as geostatistical information for detailed reservoir modeling (Doliguez et al. 1999).